To establish a large-scale, independent benchmark for evaluating auto-contouring AI with an emphasis on radiotherapy-relevant requirements: robustness to domain shift, calibration of confidence, and clinically meaningful failure modes beyond average Dice.
Author profile
Yucheng Tang, PhD
NVIDIA Corp
Benchmarking Auto-Contouring AI for Radiotherapy: Robustness, Calibration, and Failure Modes
Proffered Program · Therapy Physics
A Multicenter Pancreatic Target Segmentation Dataset for Radiotherapy and Imaging AI Benchmarking
To provide a large, diverse, and quality-controlled abdominal CT dataset with pancreas- and tumor-centric voxel-wise annotations to support benchmarking and development of AI models for pancreatic target segmentation and anatomy-aware evaluation relevant to r...
Proffered Program · Therapy Physics
An Abdominal CT Atlas for Radiotherapy Auto-Contouring: Standardization, Uncertainty, and Quality Control
To create a large, quality-controlled abdominal CT atlas that enables radiotherapy auto-contouring research by providing standardized, voxel-wise annotations across diverse institutions and by supporting uncertainty-aware expert review and benchmarking.
Poster Program · Diagnostic and Interventional Radiology Physics